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1 2022

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Improving modified policy iteration for probabilistic model checking

Mohagheghi Mohammadsadegh, Karimpour Jaber, Isazadeh Ayaz

Computer Science

2022

T. 23 (1)

63--80

Along with their modified versions, value iteration and policy iteration are well-known algorithms for the probabilistic model checking of Markov decision processes. One challenge with these methods is that they are time-consuming in most cases. Several techniques have been proposed to improve the performance of iterative methods for probabilistic model checking; however, the running times of these techniques depend on the graphical structure of the utilized model. In some cases, their performance can be worse than the performance of standard methods. In this paper, we propose two new heuristics for accelerating the modified policy iteration method. We first define a criterion for the usefulness of the computations of each iteration of this method. The first contribution of our work is to develop and use a criterion to reduce the number of iterations in modified policy iteration. As the second contribution, we propose a new approach for identifying useless updates in each iteration. This method reduces the running time of the computations by avoiding the useless updates of states. The proposed heuristics have been implemented in the PRISM model checker and applied on several standard case studies. We compare the running time of our heuristics with the running times of previous standard and improved methods. Our experimental results show that our techniques yields a significant speed-up.